Probabilistic learning method for XML annotation of documents

Abstract

A document processor includes a parser that parses a document using a grammar having a set of terminal elements for labeling leaves, a set of non terminal elements for labeling nodes, and a set of transformation rules. The parsing generates a parsed document structure including terminal element labels for fragments of the document and a nodes tree linking the terminal element labels and conforming with the transformation rules. An annotator-annotates the document with structural information based on the parsed document structure.

Description

BACKGROUND [0001] Document collections are generated and maintained by businesses, governments, and other organizations. Such document collections are typically accessible via the Internet, a local computer network, or so forth. Documents are created in various diverse formats such as word processing formats, spreadsheet formats, the Adobe portable document format (PDF), hypertext markup language (HTML), and so forth. [0002] Conversion of documents in these diverse formats to a common structured format has certain advantages. PDF and HTML are platform independent formats that enable exchange of documents across computing platforms. However, PDF and HTML are not as effective at facilitating document reuse or repurposing. HTML markup tags, for example, are not very effective at generating complex document organizing tree structures. Moreover, neither PDF nor HTML impose stringent document formatting structure requirements. For example, valid HTML documents can have missing closing tag...

AI Technical Summary

Problems solved by technology

However, this sequential approach in which the terminal element labels are first applied, followed by parsing of the tree structure, has been found to have limited robustness.

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